让自助式AI为政府服务：政府采用Agentic+AI的准备度框架

IncollaborationwithCapgemini

MakingAgenticAI

WorkforGovernment:

AReadinessFramework

INSIGHTREPORTAPRIL2026

MakingAgenticAIWorkforGovernment2

Images:GettyImages,AdobeStock

Contents

Foreword3

Executivesummary5

1

Theagenticopportunity6

1.1Fromprocessdigitizationtooutcomeorchestration7

1.2WhyagenticAImattersforgovernments9

1.3Thetacticalchallenge:wheretobegin9

2

AninnovativegovernmentreadinessframeworkforagenticAI10

2.1Afunction-basedassessmentlens11

2.2Theassessmentofpotentialagainstcomplexity13

2.3Atopographyofgovernmentreadiness15

2.4Fromglobaltopographytoregionalroadmap22

3

Learningfromsuccessfuldeployments26

Conclusion30

Appendices31

A1Methodology31

A2ComprehensivebreakdownsofagenticAIassessment33

scoresforall70coregovernmentfunctions

Contributors37

Endnotes39

Disclaimer

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©2026WorldEconomicForum.Allrightsreserved.Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,includingphotocopyingandrecording,orbyanyinformation

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MakingAgenticAIWorkforGovernment3

April2026

MakingAgenticAIWorkforGovernment:AReadinessFramework

Foreword

MartinaKlement

PermanentSecretaryforDigital

TransformationandAdministrative

Modernization;ChiefDigitalOfficer,

StateofBerlin

FabianMehring

StateMinisterforDigitalAffairs

andChiefInformationOfficer,

FreeStateofBavaria

MohamedBinTaliah

AssistantMinister,CabinetAffairsfor

GovernmentExperienceExchange

Affairs,MinistryofCabinetAffairs,

UnitedArabEmirates

Overthepastdecade,governmentsaroundthe

worldhaveinvestedheavilyindigitaltransformation–andithaspaidoff.Publicserviceshavemoved

online,data-drivendecision-makinghasgained

realtractionacrossmanyadministrations,andthegovtechmarkethasmaturedfromanicheintoa

globalforce.Technologyisnolongerasupport

functionforthestate.Itisbecomingcentraltohowgovernmentsoperate,deliverservicesandearn

thetrustoftheircitizens.

Thedriversforthisdevelopmentvary.Insome

countries,citizenexpectationshaveoutpaced

whattraditionaladministrationcandeliver,

creatingurgencytorethinkhowservicesare

designedandprovided.Inothers,demographicdeclinemeansthatdoingmorewithfewerpeopleisnotachoicebutareality.Forsome,digital

infrastructureisthebackboneofeconomic

competitiveness;forothers,itisthepathto

rebuildingpublictrustafteryearsofinstitutional

underperformance.Thestartingpointsmaybe

different,butthedirectionisthesame:technologyismovingtothecoreofgovernment.

Agenticartificialintelligence(AI)acceleratesthis

shift–andbringsanewqualitytoit.Whereearlierwavesofdigitizationmovedpaperprocessesontoscreens,agenticAIsystemscanplan,decideandactacrossentireworkflows,coordinatingstepsthatpreviouslyrequiredmanualhandoffs,interpreting

contextanddeliveringoutcomesratherthan

outputs.Forgovernmentsalreadyunderpressurefrommultipledirections,thisisnotatheoretical

prospect.Itisapracticallever.

Theopportunitiesarevast.Yet,opportunityhasneverbeenthebottleneckforgovernmenttechnology.

Therealchallengeisstrategic:understandingwhichoperationsbenefitmost,sequencingadoptionsothatearlyeffortsbuildcapabilityratherthandrainitandmaintainingpublicaccountabilitythroughout.Thisdemandsclarity,nothaste.

GovernmentsthatapproachagenticAIwith

disciplineandstrategicintentwillnotonlyimprovetheirownoperations.Theywillshapethenorms

andexpectationsforhowthistechnologyis

governedinthepublicinterest.Thatmakesthis

notjustanopportunity,butasharedresponsibility.

MakingAgenticAIWorkforGovernment4

April2026

MakingAgenticAIWorkforGovernment:AReadinessFramework

Foreword

AmmarAlkassar

Member,Board,GovTech

Deutschland

RoshanSoorunsinghGya

ChiefExecutiveOfficer,Northern

Europe,Capgemini

StephanMergenthaler

ManagingDirector;ChiefTechnology

Officer,WorldEconomicForum

Governmentsaroundtheworldareexploringthe

potentialofartificialintelligence(AI),andagenticAIinparticularisattractinggrowingattentionasawaytomovebeyondisolatedtoolstowardssystemsthat

cancoordinate,decideandactacrosscomplex

workflows.Yetformost,thisremainsdifficultto

translateintopractice.Whatisoftenmissingisa

crediblebasisfordecidingwheretobegin,what

toprioritizeandhowtomovefromexperimentationtooperationalreality.

Thismattersbecausethecostofpoor

implementationisreal.GovernmentsthatpursueagenticAIwithoutarealisticunderstanding

ofwherethepotentiallies–andwhereitdoes

not–riskinvestinginthewrongplaces,buildingsolutionsthatdonotdeliverandlosinginstitutionalconfidenceinthetechnologyalongtheway.

Enthusiasmalonedoesnotpreventthis.What

governmentsneedisenthusiasmpairedwith

strategicclarity:agroundedviewofwhich

governmentoperationsaregenuinelysuitedfor

agenticAIandwhichrequireadifferentapproach.

Thatiswhatthisreportsetsouttodo.Thestrategyandactionofpublicadministrationareshapedbyorganizationalboundaries–ministrybyministry,

departmentbydepartment.AgenticAIdoesnotworkthatway.Itoperatesacrossworkflows

thatspanorganizationallines:“cybersecurity

monitoring”,“documentlifecyclemanagement”,

“eligibilityassessment”,“frauddetection”and

“benefitcalculation”.HarnessingagenticAIthereforerequiresafundamentallydifferentunderstandingofpublicadministrationitself.

Thisreportdevelopsthatunderstanding.

Itmaps70coregovernmentworkflowsagainst

twodimensions–theopportunityforagenticAItoaddpublicvalue,andthecomplexityofdeployingitresponsibly–showingwheregovernmentscanactwithconfidence,wheretargetedpreparationisneeded,andwherecautioniswarranted.We

offerthisasasharedstartingpoint(adaptabletolocalconditions),notafinalanswer.AgenticAIwillreshapehowgovernmentsoperate.Whetherthattransformationisguidedbyevidenceandstrategicintent,orlefttochance,isachoice.Thisreport

isourcontributiontoensuringitistheformer.

MakingAgenticAIWorkforGovernment5

Executivesummary

Apioneeringframeworktohelp

governmentsmoveagenticAIfrom

experimentationtoscalablepublicvalue.

Publicexpectationsarerising,fiscalspace

istightening,andmanyadministrationsare

beingaskedtodelivermorewithless.Against

thisbackdrop,agenticartificialintelligence(AI)

representsafundamentalshiftincapability,

enablingsystemstoautonomouslyexecuteend-to-endmulti-stepworkflows,withthepotentialtotransformhowgovernmentsservecitizens.

Realizingthisopportunityrequiresstrategic,

evidence-basedadoption–groundedinaclear

assessmentofwhereagenticAIcandeliverthe

greatestpublicvalue,whatrisksmustbemanagedandwhatcapabilitiesandsafeguardsneedtobeinplacebeforedeploymentatscale.

Thisreportrespondstothatneedbyintroducing

thefirstsystematicframeworkforassessing

governmentreadinessforagenticAIandevaluatingcoreglobalgovernmentactivities(or“functions”).Theframeworkiscomplementedbyreal-world

usecasesthatgroundtheassessmentinpractice,highlightingcurrentagenticAIinitiatives.

Thestartingpoint:assessingagenticAIreadinessacross

governmentfunctions

Byfocusingonfunctions–recurringworkflowsthatcutacrossorganizationalsegregationratherthan

isolatedtasksordepartments–thisframework

providesgovernmentswithanewbasisfor

prioritizingdeploymentandimplementationatscale.

Eachfunctionisassessedacrosstwodimensions(agenticAIpotentialandimplementationcomplexity)andmappedintothreereadinessareas:

High-readinessarea:suitableforearlydeployment,withrobustsafeguards

Medium-readinessarea:suitableforphasedimplementationrequiringadditionalenablingconditions,capabilitybuildingoranalysis

Low-readinessarea:suitableformonitoring,iterativetestingandlonger-termpreparation

Thereviewof70coregovernmentfunctions

indicatesthat50%combinesignificantagenticAIpotentialwithmanageableimplementation

complexity,pointingtoasubstantialopportunity

forscaledadoptionwhereinstitutionalcapacityandsafeguardsareinplace.

Cleartakeawaysemergefromthisanalysis:

–Thinkinfunctions,notdepartments:AgenticAIoperatesinworkflowsthatcutacrossorganizationalboundaries.

–Balanceambitionwithfeasibility:

HighagenticAIpotentialshouldbe

weighedagainstimplementationcomplexitybeforeoperationalizingatscale.

–Startwherethebestoddsexist:

Buildcapabilityandconfidencewithhigh-readinessfunctionsbeforetacklingmorecomplexones.

–Localcontextdeterminessuccess:

Globalscoresarebaselines.Localinfrastructure,regulatoryenvironmentsandculturalnorms

determinewhatispossible.

–Expectthetopographytoevolve:

Reassessregularly.Functionsinthelow-

readinessareatodaymaybeinthemedium-orhigh-readinessareatomorrow.

Frominsighttoaction

Thereporttranslatesanalysisintoapractical

decision-supportframeworkforgovernments

consideringagenticAIadoption.Itprovides

astructuredapproachtoidentifyingrelevant

functionsandprioritizingopportunitiesbased

onpotentialpublicvalueandimplementation

barriers,helpingdecision-makersfocuson

whereagenticAIismostlikelytomakean

impact.Theseinsightsareintendedtoinform

subsequentchoicesongovernancedesign,

riskmanagement,pilotingandscaling.Local

adaptationisessential:eachjurisdictionmust

tailortheframeworktoitsorganizationalpriorities,digitalmaturityandregulatorycontext.

Thisframeworkisastartingpoint–atooltoinformstrategicchoices.Itisdesignedtohelppolicy-makersandindustrymovetogether

–establishingwheretobegin,howtobuild

capabilityandultimatelyhowtoconvertagenticAIopportunitiesintomeasurablepublicvalue.

MakingAgenticAIWorkforGovernment6

1

Theagenticopportunity

Agenticartificialintelligencecantransform

publicinstitutionsbyorchestratingend-to-endworkflows,shiftinggovernmentoperationsfromtaskautomationtooutcomedelivery.

MakingAgenticAIWorkforGovernment7

Agentic

systemscan

delivermeaningfulefficiencygains

whilemaintainingtheaccountability,qualityandtrustonwhichpublic

servicesdepend.

Governmenttechnologyhasbecomeafoundationaldriverofcompetitiveness,institutionalcapacity

andpublictrust.

TheGlobalPublicImpactof

GovTech:A$9.8TrillionOpportunity

estimatesa$9.8trillionopportunityfrompublic-sector

digitaltransformationby2034.1Convertingthatopportunityintosustainedimpact,however,

dependsonhowgovernmentsdesignanddeploythenextgenerationofcapabilities.

Agenticartificialintelligence(AI)isoneofthe

mostimportantleversforrealizingthispotential.

UnlikeearlierAIapplicationsthatfocusedonnarrowtaskssuchasclassification,predictionorpatternrecognition,agenticsystemscan

coordinatemulti-stepprocesses,integrate

informationacrossmultiplesourcesandadapttheiractionsbasedoncontextandevolving

conditions.Thesecapabilitiesarewellalignedwiththestructureofmanygovernment

workflows,whichtypicallyinvolvesequentialdecisions,multiplestakeholdersandthe

applicationofrulesandjudgementovertime.

Fromprocessdigitizationtooutcomeorchestration

1.1

Manygovernmentdigitaltransformationeffortshavefocusedondigitizingexistingprocesses:

movingpaperapplicationsonline,digitizing

recordsandautomatingdataentry.These

effortshavedeliveredrealvalue,buttheyhave

largelypreservedtheunderlyinglogicofpublic

administration–themediumchanges,whiletheprocessremainsfundamentallythesame.AgenticAImarksameaningfulshiftbecauseitdoesmorethananalysedata:itcanplan,decideandact

acrossmultiplestepsofaworkflow(seeBox1).

Untilnow,public-sectordigitizationhasoperated

underafamiliarconstraint:digitizingaflawed

processlargelypreserveditsflaws.AgenticAIcanhelpalleviatethisconstraintbyreasoningacross

informationandexecutinggoal-directedactions.

Whileprocessesmuststillberedesignedtounlockitsfullpotential,improvementstoexistingworkflowscandelivermeaningfulgainsinthenearterm.

–Agenticworkflowoptimization:Insteadofrequiringacompleteprocessredesignbeforeautomation,agentscandirectlycoordinate

andimproveexistingworkflows.Theirability

toorchestrateinterdependentstepscanenhanceperformanceandconsistencywithoutafullre-engineeringeffort.

–Agenticreinvention:AgenticAIenables

astructuralrethinkoflegacyprocessesbyshiftingthefocusfromtaskexecutionto

outcomeorchestration,openingthedoortonewservicemodelsandoperatingpatternsthatwerenotpossibleundertraditional,

fragmentedsystemarchitectures.

Crucially,agenticAIisbestunderstoodasamodelofaugmentationratherthanreplacement.Byhandlingroutinecoordination,acceleratingcaseprocessing

andsurfacingrelevantinformation,thesesystemsallowpublicservantstofocusonmeaningfulworkthatrequireshumanjudgement:complexcases,

policyinterpretationanddirectengagementwith

citizens.Whendeployedwithappropriateoversight,governanceandsafeguards,agenticsystemscandelivermeaningfulefficiencygainswhilemaintainingtheaccountability,qualityandtrustonwhich

publicservicesdepend.

WhatisagenticAI?

BOX1

AnAIagentisasoftwaresystemthat

autonomouslyexecutestasksbasedongoals,

applyingdecisionlogicwhileoperatingwithin

predefinedconstraints.AgenticAIreferstothe

coordinateduseofoneormoreAIagentsthat

operatewithlimitedhumaninterventionand

boundedautonomyacrossworkflows.Unlike

toolsthatrequireconstantpromptingorfollow

onlypredeterminedscripts,AIagentscan

operateinunstructuredenvironments,make

context-sensitivedecisionsinrealtime,learnfromoutcomesandtakeproactiveaction.Toensure

accountability,agenticAIistypicallydeployedwithhumanoversight.Thismaytaketheformofdirectinvolvementinkeydecisions(human-in-the-loop)

orsupervisorycontrolwiththeabilitytointervenewhenneeded(human-on-the-loop).

AgenticAIisnottheonlypathwaytoimprovingpublicsectorperformance.Predictiveanalytics,roboticprocessautomation(RPA)andgenerativeAItoolsalreadydelivermeasurableefficiency

gainsinmanygovernmentcontexts.Insomecases,theseapproachesaremoreappropriateandofferaresource-efficientinitialstepbeforeexploringmoreadvancedagenticcapabilities.Thekeyistoapplytherightlevelofintelligencetotherighttypeofworkflow.Table1contraststraditionalautomatedworkflows,generativeAIandagenticAI.

TABLE1

ComparisonofRPA,generativeAIandagenticAI

Category

GenerativeAI

Automatedworkflows(RPA)

AgenticAI

Automatesrepetitivetasksandprocesses

Primarilycreatesnewcontent

Primarilyexecutesactions

Focus

Output

Facilitatestaskexecutionacrosssoftwaresystems

Generatestext,images,code,

audio,video,etc.basedonpatternslearnedfromvasttrainingdata

Generatesnotjustcontent,butexecutesactions,decisions,andmulti-stepprocessestoreachalargerobjective

Autonomy

Hasnoautonomy,reliesentirelyonpredefinedinstructions;

designedexclusivelytoreproducetasksasdirectedbyhumans

orpreviousRPAtasks

Haslimitedautonomy,needsspecificpromptsandguidencetoproduceoutput

Hashighautonomy,capableofmakingdecisionsandexecutingtasksindependentlytopursuecomplexgoals

Learningability

Hasnolearningability

Ispre-trained,withlimitedto

noreal-timelearning;uses

memorythroughmechanismslikeconversationhistorybutdoesnotactonitdynamically

Islearningandadapting

continuously;activelyuseslong-

termmemory;dynamicallyretrievesoracquiresnewinformationand

hascontextualawareness

Interactivity

Hasnointeractivity

Cannotperformreal-time

interactions;LLMmodelitselfhasnoexternaltoolaccessunless

agenticfeaturesareadded

Interactswithexternalandinternaldata,tools,andsystemsinreal

time;isbetterpositionedtohandleerrors

Levelofcapabilities

Low.Medium.High

Source:BasedonCapgemini.(2025).RiseofagenticAI:Howtrustisthekeytohuman-AIcollaboration.

Furtherreading

ThefollowingpublicationsprovideadditionalperspectivesontheimplementationandgovernanceofagenticAI:

Ilves,I.etal.(2025).TheAgenticState:RethinkingGovernmentintheEraofAgenticAI.

GlobalGovernmentTechnologyCentreBerlinandTheWorldBank.

Capgemini.(2025).RiseofagenticAI:Howtrustisthekeytohuman-AIcollaboration

.

WorldEconomicForum.(2025).AIAgentsinAction:FoundationsforEvaluationandGovernance

.

BOX2

MakingAgenticAIWorkforGovernment8

MakingAgenticAIWorkforGovernment9

WhyagenticAImattersforgovernments

ForagenticAI

todelivervalue

withouteroding

transparency,

entrenching

bias,weakening

accountabilityorunderminingpublictrust,strategic

sequencingandgovernance

capacityarekey.

1.2

Governmentsfaceadecisivemomentshapedbybothaccelerationandconstraint.AgenticAIhasreachedoperationalmaturityfor

publicsectoruse,supportedbyincreasinglyrobustcommercialecosystems.Yetfiscal

pressureandservicedemandscontinuetointensify.Themandatetodomorewithlesshasbecomeastructuralrequirement.

ArecentsurveyconductedbyCapgemini2–

spanning350public-sectororganizations

globallyacrosssixsectors(predominantlypublicadministration,taxandcustoms)indicatesthat90%ofsurveyedinstitutionsplantoexplore

ordeployagenticAIwithintwotothreeyears.

Thisreflectsmorethantechnologicaloptimism.

Itsignalsrecognitionthatgovernmentscannot

meetcurrentmandateswithcurrentoperating

modelsalone.AgenticAIoffersacrediblepath

tooperationalimprovementatscale,andthe

conditionsforsuccessfuldeploymentarestrongerthantheyhaveeverbeen.Atthesametime,

safeguardingsovereigncontrolovercriticalelementsofAIsystems–acrossregulatory,operational,

technologicalandgeopoliticaldimensions–willbeessentialtoensuringthetransformationremainsalignedwithpublic-interestrequirements.

Thistransitionmarksabroadershifttowardswhatcanbedescribedasanagenticstate:

disciplineddeploymentofagenticAIacross

coregovernmentworkflows.Anagenticstate

representsamodelofpublicadministrationinwhichAI-enabledsystemsautonomouslycoordinate

andexecuteacrossinstitutionalboundaries,

reduceadministrativelatencyandenablemoreproactive,outcome-orientedservicedelivery.

Thebenefitscanbeassessedthroughvarious

valuemetrics,includinggreaterconsistency

indecision-making,improvedcompliance,

timesavings,reducederrors,improvedcitizen

satisfactionandequityimpacts.Definingthese

earlyprovidesaclearframeforevaluatingwhere

agenticAIcreatestangibleadministrativevalue.

Yetthistransformationcarriesrealrisks.ForagenticAItodelivervaluewithouterodingtransparency,

entrenchingbias,weakeningaccountabilityor

underminingpublictrust,strategicsequencingandgovernancecapacityarekey,anditsautonomy

mustbeintentionallylimited.Agentsshouldoperatewithinclearlycircumscribedmandates,escalate

tohumanoversightwhennecessaryandmaketheirdecisionstransparent.

1.3

Thetacticalchallenge:wheretobegin

Givenongoingtechnicalprogress,thecentralquestionforgovernmentsisnowstrategic

ratherthanpurelytechnical:whichactivities

areappropriateforagenticautomation,whichoffersufficientpublicvaluereturns–meaning

economicreturnsaswellastangiblesocial

benefits–tojustifyinvestmentandwhich

involverisksorcomplexitiesthatrequirecaution.

Thecostsofbotherrorandinactionarereal.Delaycanforcedependenceonexternallydeveloped

solutionsthatdonotreflectlocalneedsorpolicy

priorities;precipitousactioncanleadtofragmentedpilotsthatdrainresources,erodeinstitutional

confidenceandimpedefutureAIinitiativeswithoutdeliveringresults.

Whilemanypublicsectorinstitutionshaverecognizedtheopportunity,Gartner®projectsthat“over40%

ofagenticAIprojectswillbecancelledbytheendof2027,duetoescalatingcosts,unclearbusinessvalueorinadequateriskcontrols”.3Thisprovidesacautionarynote,underscoringhowenthusiasmcanoutpacestrategicplanning.Asystematic,

evidence-basedapproachisthereforeessentialtoidentifyingwhereagenticAIcandeliverlong-termpublicvalue.

MakingAgenticAIWorkforGovernment10

2

Aninnovativegovernmentreadinessframework

foragenticAI

ByevaluatingtheAIreadinessofcore

governmentfunctions,theframeworkhighlightswhereAIagentscanbedeployedandwhere

risksoutweighbenefits.

MakingAgenticAIWorkforGovernment11

governmentscanactwithconfidence,wheretheyshouldinvestinpreparationandwherecaution

iswarranted–helpingthemtranslatethebroadpromiseofagenticAIintostrategicdecisions.

Thereportintroducesaframeworkwithfour

buildingblockstoguidedecision-makingon

agenticAIingovernment(seeFigure1).It

assessesfunctionsagainstbothitspotentialanditscomplexity,producingaclearpictureofwhere

FIGURE1ThegovernmentreadinessframeworkforagenticAI

ThereportdefineshowgovernmentsshouldevaluateandscoreagenticAIreadiness.

TheresultsarevisualizedasanagenticAIreadinessmap,showingwhereto

act,prepareandmonitor.

Settingof

Scoringof

Creationof

assessmentcriteria

government

topographywiththree

intwodimensions

readiness

prioritizationareas

1

23

4

Afunction-basedassessmentlens

Thereportspecifies

whatunitgovernmentsneedtoevaluatefor

agenticAIdecisions.

Theassessmentof

potentialagainstcomplexity

Atopographyof

governmentreadiness

Fromglobaltopographytoregionalroadmap

What?

Theresultsmustbe

adaptedtolocalcontexttobecomeactionable.

Adaptiontolocalcontextinsixsteps

Useofadomain-agnosticunit

ofanalysis

How?

1Assessbaselines

2Reassessfunctions

3Sequence

implementation

4Test

5Scale

6Iterate

70recurringcorefunctionsinninecategories

High-readinessarea(deploy)

Medium-readinessarea(prepare)

Low-readinessarea(monitor)

1Potential

(opportunity)

2Complexity(barriers)

1

Potential–

complexity=agenticAI

readiness

2

3

Afunction-basedassessmentlens

2.1

EarlierAI

applications

weredesigned

fornarrowtaskswithinasingle

context.AgenticAI,bycontrast,

cancoordinate

entireworkflowsthatspanmultiplesystemsand

decisionpoints.

Theframeworkmapsgovernmentactivitiesnotbydepartmentbutbyfunction:therecurringworkflowsthatcutacrossorganizationalboundaries.In

thisreport,thetermfunctionisnotusedinthe

traditionalsenseofadepartmentunit–e.g.humanresources(HR),finance,logistics.Instead,itreferstooutcome-orientedoperationalactivitiesthat

encompassoneormoreend-to-endworkflows.

Thinkingagentic:anew

lensforpublicadministration

AgenticAIrequiresadifferentlensforthinking

abouthowpublicadministrationisorganizedandevaluated.Traditionalapproachestotechnologyimplementationingovernmenttendtofollow

organizationalboundaries:assessingdigitalizationdepartmentbydepartment,orcataloguingAI

usecasessectorbysector.Theseapproaches

madesenseforearliergenerationsoftechnology,butnotforadaptivesystemsthatoperateacrossworkflows,institutionsanddecisioncycles.

Thedistinctionbecomesclearwhenexamininghowagenticsystemsfunction.EarlierAIapplications

weredesignedfornarrowtaskswithinasingle

context–classifyingdocuments,predictingdemandorrecognizingpatterns.AgenticAI,bycontrast,

cancoordinateentireworkflowsthatspanmultiplesystemsanddecisionpoints.

Forexample,atraditionalAIsystemusedin

applicationprocessingmightautomateonestep,

suchasextractingdatafromsubmitteddocumentsorflaggingincompleteformsforreview.An

agenticAIsystem,however,couldmanagethe

fullend-to-endworkflow:receivingandlogging

submissions,cross-checkinginformationacross

databases,orchestratingrequiredstepsandroutingcasestotheappropriateauthority.

MakingAgenticAIWorkforGovernment12

Governments

canidentifywhereagenticAIis

bothfeasible

andvaluable,andcreateopportunitiesforreuserather

thangeneratingfragmented

pilotss